A Novel Choquet Integral Composition Forecasting Model for Time Series Data Based on Completed Extensional L-Measure

Abstract

In this study, based on the Choquet integral with respect to complete extensional L-measure and M-density, a novel composition forecasting model which composed the time series model , the exponential smoothing model and GM(1,1) forecasting model was proposed. For evaluating this improved composition forecasting model, an experiment with the data of the grain production in Jilin during 1952 to 2007 by using the sequential mean square error was conducted. Based on the M-density and N- density, the performances of Choquet integral composition forecasting model with the completed extensional L-measure, extensional L-measure, L-measure, Lambda-measure and P-measure, respectively, a ridge regression composition forecasting model and a multiple linear regression composition forecasting model and the traditional linear weighted composition forecasting model were compared. The experimental results showed that the Choquet integral composition forecasting model with respect to the completed extensional L-measure and M-density outperforms other ones. Furthermore, for each fuzzy measure, including the completed extensional L-measure, extensional L-measure, L-measure, Lambda-measure and P-measure, respectively, the Choquet integral composition forecasting model based on M-density is better than the one based on N-density.